Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhang, Jianxin, Liu, Kai, and Wang, Ming, 2021. Downscaling Groundwater Storage Data in China to a 1-km Resolution Using Machine Learning Methods. Remote Sensing, 13(3):523, doi:10.3390/rs13030523.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2021RemS...13..523Z,
author = {{Zhang}, Jianxin and {Liu}, Kai and {Wang}, Ming},
title = "{Downscaling Groundwater Storage Data in China to a 1-km Resolution Using Machine Learning Methods}",
journal = {Remote Sensing},
keywords = {groundwater storage, terrestrial water storage, downscaling, random forest, XGBoost, GRACE, GLDAS},
year = 2021,
month = feb,
volume = {13},
number = {3},
eid = {523},
pages = {523},
abstract = "{High-resolution and continuous hydrological products have tremendous
importance for the prediction of water-related trends and
enhancing the capability for sustainable water resources
management under climate change and human impacts. In this
study, we used the random forest (RF) and extreme gradient
boosting (XGBoost) methods to downscale groundwater storage
(GWS) from 1{\textdegree} (\raisebox{-0.5ex}\textasciitilde110
km) to 1 km by downscaling Gravity Recovery and Climate
Experiment (GRACE) and Global Land Data Assimilation System
(GLDAS) data from 1{\textdegree}
(\raisebox{-0.5ex}\textasciitilde110 km) and 0.25{\textdegree}
(\raisebox{-0.5ex}\textasciitilde25 km) respectively, to 1 km
for China. Three evaluation metrics were employed for the
testing dataset for 2004-2016: The R$^{2}$ ranged from 0.77-0.89
for XGBoost (0.74-0.86 for RF), the correlation coefficient (CC)
ranged from 0.88-0.94 for XGBoost (0.88-0.93 for RF) and the
root-mean-square error (RMSE) ranged from 0.37-2.3 for XGBoost
(0.4-2.53 for RF). The R$^{2}$ of the XGBoost models for GLDAS
was 0.64-0.82 (0.63-0.82 for RF), the CC was 0.80-0.91
(0.80-0.90 for RF) and the RMSE was 0.63-1.75 (0.63-1.77 for
RF). The downscaled GWS derived from GRACE and GLDAS were
validated using in situ measurements by comparing the time
series variations and the downscaled products maintained the
accuracy of the original data. The interannual changes within 9
river basins between pre- and post-downscaling were consistent,
emphasizing the reliability of the downscaled products.
Ultimately, annual downscaled TWS, GLDAS and GWS products were
provided from 2004 to 2016, providing a solid data foundation
for studying local GWS changes, conducting finer-scale
hydrological studies and adapting water resources management and
policy formulation to local condition.}",
doi = {10.3390/rs13030523},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13..523Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Generated by
bib2html_grace.pl
(written by Patrick Riley
modified for this page by Volker Klemann) on
Thu Apr 10, 2025 10:40:58
GRACE-FO
Thu Apr 10, F. Flechtner